Login using

You can login by using one of your existing accounts.

We will be provided with an authorization token (please note: passwords are not shared with us) and will sync your accounts for you. This means that you will not need to remember your user name and password in the future and you will be able to login with the account you choose to sync, with the click of a button.

In today’s era of information explosion, extracting entities and their relations in large-scale, unstructured collections of text to better represent knowledge has emerged as a daunting challenge in biomedical text mining. To respond to the demand to automatically extract scientific knowledge with higher precision, the public knowledge discovery tool PKDE4J (Song et al., 2015) was proposed as a flexible text-mining tool. In this study, we propose an extended version of PKDE4J to represent scientific knowledge for literature-based knowledge discovery. Specifically, we assess the performance of PKDE4J in terms of three extraction tasks: entity, relation, and event detection. We also suggest applications of PKDE4J along three lines: 1) knowledge search, 2) knowledge linking, and 3) knowledge inference. We first describe the updated features of PKDE4J and report on tests of its performance. With additional options in the processes of named entity extraction, verb expansion, and event detection, we expect that the enhanced PKDE4J can be utilized for literature-based knowledge discovery.